Unmanned aerial vehicles or drones are increasingly employed for performing various tasks such as surveillance, search and rescue operations, surveying and mapping, delivering cargo, aerial photography, and so forth. One such area, where drones may be effectively utilized, is inspection and maintenance of complex three-dimensional (3D) structures (e.g., building, factories, machinery, etc.). As will be appreciated, a 3D structure may have many obscure portions, which make it tedious and time consuming for a human to perform a task with precision. In contrast, agile and compact drones may easily access and perform various tasks in such obscure portions of the 3D structure.
However, existing techniques are limited in their ability and usefulness. For example, existing techniques for performing a task (e.g., painting) require a user to remotely maneuver the drone to the designated area and cause it to perform the desired task. Thus, each time a task is to be performed, the user has to provide detailed instruction to the aerial operation system. Further, existing techniques are not capable of learning context from their daily operations. In short, existing techniques do not provide for an autonomous performance of a task in an improvised manner by learning from its own performance in real-time.